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New research shows LLM agents improve with tool refusal signals

By PulseAugur Editorial · [1 sources] ·

A new arXiv paper explores how large language models, referred to as 'frozen agents,' react when their retrieval tools provide no relevant information. Researchers found that a simple, unannounced refusal signal significantly improved abstention rates on unanswerable questions for models like Qwen3 and Claude Haiku 4.5, drastically reducing incorrect answers. The study also highlighted that the wording of the refusal signal impacts agent behavior, with explanations proving more effective than bare tokens or soft warnings. AI

IMPACT Improved agent abstention rates could lead to more reliable AI systems that better indicate when they lack information.

RANK_REASON The cluster contains a research paper published on arXiv detailing experimental findings on LLM behavior. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.IR (Information Retrieval) →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New research shows LLM agents improve with tool refusal signals

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The cluster contains a research paper published on arXiv detailing experimental findings on LLM behavior. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.IR (Information Retrieval) TIER_1 English(EN) · Vinoth Selvendran ·

    Search Engines Never Say No: How Frozen Agents React When the Retrieval Tool Refuses

    A search tool never says no: it returns its top-k passages even when the index holds no answer, so the agent sees irrelevant text instead of a miss signal. We ask what frozen search agents do when the tool refuses instead. On an index-hole testbed (257 NQ and 300 HotpotQA questio…

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